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https://issues.apache.org/jira/browse/LUCENE-9004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16955513#comment-16955513
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Michael Sokolov commented on LUCENE-9004:
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[~dsmiley] I didn't reply for a while because I'm not sure what the best
approach is to dealing with random access. The problems with the current
approach are:
1. The DocValues API contract forces you to create a new DV iterator when you
want to go backwards, wasting objects and any work done to initialize the
iterator (both probably small, but you do this pretty often)
2. The DocValues on-disk encoding is not the most efficient for random access;
it is heavily optimized for efficient forward-only iteration.
3. Updates to DocValues are handled by allocating objects that are applied on
merge, but we make a great many updates to the graph while indexing and this
isn't tenable, so we need to be able to update in place.
For the implementation I think we'd want to have some kind of efficient lookup
addressing structure mapping docid -> values payload, with the values
themselves in a separate densely-packed block. The simplest thing would be
indirection through an array with an entry (int) for every document, but that
could be made more space-efficient for cases where the doc values are sparsely
populated. I guess such a concept could be applied to any DocValues, and we
could use DocValues API to access such a thing, but since most use cases are
handled well with the forward iteration, it would make sense to me to explore
this implementation in a separate special-use project? Also I haven't yet seen
any particular benefit in using the DocValues API for graphs: might as well
make something new?
The proposal to create a random-access DocValues would require no change to the
API (we have all the methods we need), but the assumption that docids go
forwards is deeply baked in at this point not only in the current
implementations, but also in the test framework and I think in some
abstractions (like DocValuesIterator) that are shared by all implementations.
Maybe that could be relaxed in a sane way? I haven't really tried to see. Or we
could make something new. Would that be a Format specific to this graph use
case? Or a RandomAccessDocValues that can support a few different cases? I tend
to not want to create abstractions until we have at least a few concrete cases
for them.
[~tomoko] - thanks for the excellent exposition (and picture!). You are right,
the branch I posted doesn't encode that hierarchical structure yet. I think it
could be done using multiple {{SortedNumericDocValues}} fields, one for each
level. I'm not saying this is a *good* design, merely that it's possible.
I think the idea of a custom format to represent the graph is pretty much what
you are describing? It gives you the freedom to just do something new. As I
understand it, existing formats are things like postings, doc-values, points,
or stored fields - a separate on-disk "file" effectively.
> Approximate nearest vector search
> ---------------------------------
>
> Key: LUCENE-9004
> URL: https://issues.apache.org/jira/browse/LUCENE-9004
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Michael Sokolov
> Priority: Major
> Attachments: hnsw_layered_graph.png
>
>
> "Semantic" search based on machine-learned vector "embeddings" representing
> terms, queries and documents is becoming a must-have feature for a modern
> search engine. SOLR-12890 is exploring various approaches to this, including
> providing vector-based scoring functions. This is a spinoff issue from that.
> The idea here is to explore approximate nearest-neighbor search. Researchers
> have found an approach based on navigating a graph that partially encodes the
> nearest neighbor relation at multiple scales can provide accuracy > 95% (as
> compared to exact nearest neighbor calculations) at a reasonable cost. This
> issue will explore implementing HNSW (hierarchical navigable small-world)
> graphs for the purpose of approximate nearest vector search (often referred
> to as KNN or k-nearest-neighbor search).
> At a high level the way this algorithm works is this. First assume you have a
> graph that has a partial encoding of the nearest neighbor relation, with some
> short and some long-distance links. If this graph is built in the right way
> (has the hierarchical navigable small world property), then you can
> efficiently traverse it to find nearest neighbors (approximately) in log N
> time where N is the number of nodes in the graph. I believe this idea was
> pioneered in [1]. The great insight in that paper is that if you use the
> graph search algorithm to find the K nearest neighbors of a new document
> while indexing, and then link those neighbors (undirectedly, ie both ways) to
> the new document, then the graph that emerges will have the desired
> properties.
> The implementation I propose for Lucene is as follows. We need two new data
> structures to encode the vectors and the graph. We can encode vectors using a
> light wrapper around {{BinaryDocValues}} (we also want to encode the vector
> dimension and have efficient conversion from bytes to floats). For the graph
> we can use {{SortedNumericDocValues}} where the values we encode are the
> docids of the related documents. Encoding the interdocument relations using
> docids directly will make it relatively fast to traverse the graph since we
> won't need to lookup through an id-field indirection. This choice limits us
> to building a graph-per-segment since it would be impractical to maintain a
> global graph for the whole index in the face of segment merges. However
> graph-per-segment is a very natural at search time - we can traverse each
> segments' graph independently and merge results as we do today for term-based
> search.
> At index time, however, merging graphs is somewhat challenging. While
> indexing we build a graph incrementally, performing searches to construct
> links among neighbors. When merging segments we must construct a new graph
> containing elements of all the merged segments. Ideally we would somehow
> preserve the work done when building the initial graphs, but at least as a
> start I'd propose we construct a new graph from scratch when merging. The
> process is going to be limited, at least initially, to graphs that can fit
> in RAM since we require random access to the entire graph while constructing
> it: In order to add links bidirectionally we must continually update existing
> documents.
> I think we want to express this API to users as a single joint
> {{KnnGraphField}} abstraction that joins together the vectors and the graph
> as a single joint field type. Mostly it just looks like a vector-valued
> field, but has this graph attached to it.
> I'll push a branch with my POC and would love to hear comments. It has many
> nocommits, basic design is not really set, there is no Query implementation
> and no integration iwth IndexSearcher, but it does work by some measure using
> a standalone test class. I've tested with uniform random vectors and on my
> laptop indexed 10K documents in around 10 seconds and searched them at 95%
> recall (compared with exact nearest-neighbor baseline) at around 250 QPS. I
> haven't made any attempt to use multithreaded search for this, but it is
> amenable to per-segment concurrency.
> [1]
> https://www.semanticscholar.org/paper/Efficient-and-robust-approximate-nearest-neighbor-Malkov-Yashunin/699a2e3b653c69aff5cf7a9923793b974f8ca164
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